spatially classification decision trees: fundamentals and some extensions

In classical statistics, observations of a random variable are commonly assumed to be independent and identically distributed. most statistical learning techniques such as classification and regression trees (cart) assume independent samples to compute classification rules. but this assumption is often violated in spatial datasets, and it may not be efficient for analyzing spatial data. the cart algorithm is adapted to the case of spatially dependent samples by three strategies; the first one is the weighting of the data according to their spatial pattern, the second is spatial entropy used as the splitting criterion and in the third, we combine these two strategies to achieve more accuracy. this method is evaluated on a classical dataset to highlight its advantages and drawbacks.